9 citations · 11 across the 7 of their papers we have counts for
6 papers
Efficient Bayesian functional principal component analysis of irregularly-observed multivariate curves
Tui Nolan, Sylvia Richardson, Hélène Ruffieux
The analysis of multivariate functional curves has the potential to yield important scientific discoveries in domains such as healthcare, medicine, economics and social sciences. H…
Bayesian Functional Principal Components Analysis via Variational Message Passing
Tui H. Nolan, Jeff Goldsmith, David Ruppert
Functional principal components analysis is a popular tool for inference on functional data. Standard approaches rely on an eigendecomposition of a smoothed covariance surface in o…
Measure Transport with Kernel Stein Discrepancy
Matthew A. Fisher, Tui Nolan, Matthew M. Graham +2
Measure transport underpins several recent algorithms for posterior approximation in the Bayesian context, wherein a transport map is sought to minimise the Kullback--Leibler diver…
Streamlined Variational Inference for Higher Level Group-Specific Curve Models
M. Menictas, T. H. Nolan, D. G. Simpson +1
A two-level group-specific curve model is such that the mean response of each member of a group is a separate smooth function of a predictor of interest. The three-level extension…
Streamlined Computing for Variational Inference with Higher Level Random Effects
Tui H. Nolan, Marianne Menictas, Matt P. Wand
We derive and present explicit algorithms to facilitate streamlined computing for variational inference for models containing higher level random effects. Existing literature, such…
Solutions to Multilevel Sparse Matrix Problems
Tui H. Nolan, Matt P. Wand
We define and solve classes of sparse matrix problems that arise in multilevel modeling and data analysis. The classes are indexed by the number of nested units, with two-level pro…